热释电红外信号特征分析及人体识别方法研究
|
摘要
随着经济的发展与科技的进步,人们对社会公共安全和家居环境安全提出了更高的要求。政府开展的“平安城市”建设,更是将安防工程的建设推向了一个新的高潮。“平安城市”的核心系统包括电视监控系统、电子巡查系统和入侵报警系统等。“平安城市”的建设给安防产业带来了巨大的商机,同时也对各种安防产品提出了更高的技术要求。热释电红外(Pyroelectric Infrared, PIR)探测器作为入侵报警系统中最常见的监控产品之一,它具有功耗低、性能稳定、成本低廉及良好的环境适应性等优点,在家庭、社区和工商业等安防领域有着广泛的应用。但是,现有各种PIR探测器所存在的高误报率的缺点限制了它的应用场合。通过深入的研究发现:虽然PIR探测器自身原理和结构设计存在一定的局限性,但更重要的是缺乏对PIR探测器输出信号的有效分析,没有对不同辐射源的PIR信号进行深入的特征挖掘。因此,将信号处理与模式识别方法引入到PIR信号的分析中,不仅对提升PIR探测器的检测性能具有一定的应用价值,而且对安防系统中一维信号的分析及识别也具有重要的学术意义。
本论文在国家“863”计划,国家“十一五”基础研究项目及重庆市科技攻关项目等课题的支持下,针对PIR探测器的高误报率问题,以不同红外辐射源的PIR信号为研究对象,以信号处理和模式识别的理论和方法为手段,深入系统地研究了PIR信号的预处理方法、特征提取方法与特征融合方法,并在此基础上,提出了降低PIR探测器误报率的人体PIR信号的识别算法,为提高PIR探测器的检测性能提供了理论依据和可行的实施方案。
本论文主要开展了以下四个方面的探索性研究工作:
(1)在深入研究PIR探测器特性的基础上,建立了不同辐射源的等效模型,推导了不同等效模型的有效辐射面积与辐射源位置关系的表达式,分析了人体和非人体PIR信号的差异性。通过仿真得到了PIR探测器的理想输出波形,仿真数据与实际获取的数据具有很好的相似性。这不仅为进一步研究去噪方法提供了可信的“无污染”的原始信号,而且为后续研究和设计PIR探测器提供了有意义的参考信息。最后,验证了PIR信号的非平稳随机性,为研究PIR信号的特征提取方法提供了依据。
(2)鉴于人体和非人体PIR信号在时频域上能量分布的差异性,本论文提出了一种基于熵理论的小波包熵PIR信号特征提取方法。小波包分解在频域具有更精细的划分,将Shannon信息熵与小波包分解相结合,可以获取表征PIR信号在时频域中复杂度的特征。研究表明:选择与PIR信号具有相同对称特性的db1小波分解后得到的小波包熵具有较好的分类效果。而人体PIR信号的小波包熵在0-2.5Hz频段显著小于该频段非人体PIR信号的小波包熵值,这表明人体PIR信号的有序性更好。
(3)由于实小波变换对PIR信号的数据敏感,即输入数据的变化会对小波系数产生不可预测的结果。因此提出一种基于双密度双树复小波变换(DD-DT CWT)小波熵特征的PIR信号特征提取方法。DD-DT CWT具有良好的平移不变特性,抗混叠性及计算效率高等优点,利用DD-DT CWT小波系数Shannon熵在保留PIR信号近似周期性变化特征的同时,能有效提取人体和非人体PIR信号的时频特征差异,为准确识别不同辐射源提供有效的判别信息。研究表明:4层分解后DD-DT CWT小波系数Shannon熵识别率为87.3%。
(4)为了进一步提高识别率,提出了一种基于典型相关分析(CCA)的PIR信号特征融合方法。该方法将两组PIR信号的特征矢量间的相关性特征作为判别信息,既达到了信息融合的目的,又消除了特征之间的信息冗余,为两组PIR信号特征融合后用于分类识别提供了新的途径。研究发现,将PIR信号的全局特征划分为不同的子段,然后再将全局特征与子段特征进行CCA融合,可以获得具有更好分类性能的特征描述。实验研究表明:采用DD-DT CWT小波系数Shannon熵特征与其子段特征进行CCA特征融合后,识别率可达到94.3%,比单独采用该特征识别率提高了7.0%。
本论文所提出的3种特征提取及识别方法均能有效地改善现有PIR探测器的检测性能。此外,通过对3种特征提取方法的横向比较发现,基于CCA特征融合的PIR信号识别方法具有最高的人体检测率和最低的误报率。
With the development of technology and economy, people put forward higher requirement for the safety of public and home evironment. The Urban Safety Program carried out by government agencies accelerates the development of safe protection engineering. The key parts of the Urban Safety Program consist of television monitoring system, electronic patrol system, human intrusion detection system, and so on. The Urban Safety Program brings great business opportunities, and raises higher technical demands on all kinds of security products at the same time. The pyroelectric infrared (PIR) detectors are most widely used in home and public security system for their low cost, low power consumption, statble performance and excellent environmental adaptability. However, the high false alarm rate of the existing PIR detectors has limited their applications. Based on thoroughly analyses, it’s found out that besides the limitation of their mechanism and structure design, there is no effective analysis and feature extraction for PIR signals of different infrared sources. Therefore, introducing the method of signal processing and pattern recognition into the analysis of PIR signal is not only valuable for improving the performance of PIR detector, but also significant for analyzing one dimension signals in security system.
The research proposed in this dissertation is supported by the National High-Tech Research and Development Plan of China, and by the Basic Research Project of the‘Eleventh Five-Year-Plan’of China, and by Key Research Project of the Natural Science Foundation of Chongqing Municipality of China. This dissertation focus on the false alarm rate of the widely used PIR detectors, the study subjects are the PIR signals of different infrared sources collected in different experiments. The preprocessing method, feature extraction method and feature fusion method for PIR signal are studied systematically. The human and non-human recognition mthod for decreasing the false alarm rate has been proposed, so that the practical implementation method for improving the performance is presented.
Four main explorative researches on PIR signal recognition are made in this papar:
(1) Based on deep research on performances of the PIR detector, the equivalent model of different infrared radiation sources are created, and the relational expressions of effective radiation area and position are derived, and feature differences between human and non-human PIR signal are analyzed. Then the ideal output signal of a PIR detector is simulated. The simulation signal and the collected signal has good similarity, which can supply pure original signal for selecting denoising method, and can provide reference for designing PIR detectors with better performance. At last, the PIR signal is proved to be non-stationary which provides significant reference for feature extraction.
(2) In view of the fact there are differences between human and non-human PIR signal in power distribution in time-frequency domain, wavelet packet entroy (WPE) is proposed for feature extraction.Wavelet packet decomposition has finer and adjustable resolution at high frequency bands which extracts more detail features of different infrared sources. Combining the Shannon entropy with the wavelet packet decompaositon, the aquired features descript the complexity of different PIR signals. Experimental results show that db1 wavelet which holds similary symmetry with a PIR signal has best recognition ability. The WPE value of human body is significiantly smaller than that of non-human body in frequency band from 0Hz to 2.5Hz, which demonstrates that the human PIR signal is more orderly.
(3) Because the real wavelet decomposition is shift sensitve, that is, small fluctuation will lead to unpredictable results. Double density dual tree complexity wavelet transform (DD-DT CWT) wavelet entropy is proposed for extracting features of PIR signals. DD-DT CWT has good properties of shift-invariant, anti aliasing and high calculation efficiency. Therefore, DD-DT CWT wavelet entropy preserves the properties of approximately periodic of PIR signal, and extracts features which are can be used as discrimination information for different infrared soruces. Experimental results show that the recognition rate is 87.3% when the decomposition level is 4.
(4) In order to improve the recognition ability, canonical correlation analysis (CCA) for PIR signal feature fusion is proposed. The proposed method uses correlation features of two groups of feature vectors as effective discriminant information, so it is not only suitable for information fusion, but also eliminates the redundant information among the features. This is a new way to classifiacation and recongiton for PIR signals. Better feature description for classification can be obtained by fusing the local and global features of PIR signals. Experimental results show the recognition rate of DD-DT CWT wavelet entropy fusing with its own sub-pattern based on CCA method can reach to 94.3%, which is 7.0% higher than that of using single feature.
The three feature extraction and recognition methods proposed in this dissertation are effective for improving the performance of the existing PIR detector. In addition, by the comparison of the three methods, the CCA feature fusion method has the best human recognition ability and the lowest false alarm rate.
本论文在国家“863”计划,国家“十一五”基础研究项目及重庆市科技攻关项目等课题的支持下,针对PIR探测器的高误报率问题,以不同红外辐射源的PIR信号为研究对象,以信号处理和模式识别的理论和方法为手段,深入系统地研究了PIR信号的预处理方法、特征提取方法与特征融合方法,并在此基础上,提出了降低PIR探测器误报率的人体PIR信号的识别算法,为提高PIR探测器的检测性能提供了理论依据和可行的实施方案。
本论文主要开展了以下四个方面的探索性研究工作:
(1)在深入研究PIR探测器特性的基础上,建立了不同辐射源的等效模型,推导了不同等效模型的有效辐射面积与辐射源位置关系的表达式,分析了人体和非人体PIR信号的差异性。通过仿真得到了PIR探测器的理想输出波形,仿真数据与实际获取的数据具有很好的相似性。这不仅为进一步研究去噪方法提供了可信的“无污染”的原始信号,而且为后续研究和设计PIR探测器提供了有意义的参考信息。最后,验证了PIR信号的非平稳随机性,为研究PIR信号的特征提取方法提供了依据。
(2)鉴于人体和非人体PIR信号在时频域上能量分布的差异性,本论文提出了一种基于熵理论的小波包熵PIR信号特征提取方法。小波包分解在频域具有更精细的划分,将Shannon信息熵与小波包分解相结合,可以获取表征PIR信号在时频域中复杂度的特征。研究表明:选择与PIR信号具有相同对称特性的db1小波分解后得到的小波包熵具有较好的分类效果。而人体PIR信号的小波包熵在0-2.5Hz频段显著小于该频段非人体PIR信号的小波包熵值,这表明人体PIR信号的有序性更好。
(3)由于实小波变换对PIR信号的数据敏感,即输入数据的变化会对小波系数产生不可预测的结果。因此提出一种基于双密度双树复小波变换(DD-DT CWT)小波熵特征的PIR信号特征提取方法。DD-DT CWT具有良好的平移不变特性,抗混叠性及计算效率高等优点,利用DD-DT CWT小波系数Shannon熵在保留PIR信号近似周期性变化特征的同时,能有效提取人体和非人体PIR信号的时频特征差异,为准确识别不同辐射源提供有效的判别信息。研究表明:4层分解后DD-DT CWT小波系数Shannon熵识别率为87.3%。
(4)为了进一步提高识别率,提出了一种基于典型相关分析(CCA)的PIR信号特征融合方法。该方法将两组PIR信号的特征矢量间的相关性特征作为判别信息,既达到了信息融合的目的,又消除了特征之间的信息冗余,为两组PIR信号特征融合后用于分类识别提供了新的途径。研究发现,将PIR信号的全局特征划分为不同的子段,然后再将全局特征与子段特征进行CCA融合,可以获得具有更好分类性能的特征描述。实验研究表明:采用DD-DT CWT小波系数Shannon熵特征与其子段特征进行CCA特征融合后,识别率可达到94.3%,比单独采用该特征识别率提高了7.0%。
本论文所提出的3种特征提取及识别方法均能有效地改善现有PIR探测器的检测性能。此外,通过对3种特征提取方法的横向比较发现,基于CCA特征融合的PIR信号识别方法具有最高的人体检测率和最低的误报率。
With the development of technology and economy, people put forward higher requirement for the safety of public and home evironment. The Urban Safety Program carried out by government agencies accelerates the development of safe protection engineering. The key parts of the Urban Safety Program consist of television monitoring system, electronic patrol system, human intrusion detection system, and so on. The Urban Safety Program brings great business opportunities, and raises higher technical demands on all kinds of security products at the same time. The pyroelectric infrared (PIR) detectors are most widely used in home and public security system for their low cost, low power consumption, statble performance and excellent environmental adaptability. However, the high false alarm rate of the existing PIR detectors has limited their applications. Based on thoroughly analyses, it’s found out that besides the limitation of their mechanism and structure design, there is no effective analysis and feature extraction for PIR signals of different infrared sources. Therefore, introducing the method of signal processing and pattern recognition into the analysis of PIR signal is not only valuable for improving the performance of PIR detector, but also significant for analyzing one dimension signals in security system.
The research proposed in this dissertation is supported by the National High-Tech Research and Development Plan of China, and by the Basic Research Project of the‘Eleventh Five-Year-Plan’of China, and by Key Research Project of the Natural Science Foundation of Chongqing Municipality of China. This dissertation focus on the false alarm rate of the widely used PIR detectors, the study subjects are the PIR signals of different infrared sources collected in different experiments. The preprocessing method, feature extraction method and feature fusion method for PIR signal are studied systematically. The human and non-human recognition mthod for decreasing the false alarm rate has been proposed, so that the practical implementation method for improving the performance is presented.
Four main explorative researches on PIR signal recognition are made in this papar:
(1) Based on deep research on performances of the PIR detector, the equivalent model of different infrared radiation sources are created, and the relational expressions of effective radiation area and position are derived, and feature differences between human and non-human PIR signal are analyzed. Then the ideal output signal of a PIR detector is simulated. The simulation signal and the collected signal has good similarity, which can supply pure original signal for selecting denoising method, and can provide reference for designing PIR detectors with better performance. At last, the PIR signal is proved to be non-stationary which provides significant reference for feature extraction.
(2) In view of the fact there are differences between human and non-human PIR signal in power distribution in time-frequency domain, wavelet packet entroy (WPE) is proposed for feature extraction.Wavelet packet decomposition has finer and adjustable resolution at high frequency bands which extracts more detail features of different infrared sources. Combining the Shannon entropy with the wavelet packet decompaositon, the aquired features descript the complexity of different PIR signals. Experimental results show that db1 wavelet which holds similary symmetry with a PIR signal has best recognition ability. The WPE value of human body is significiantly smaller than that of non-human body in frequency band from 0Hz to 2.5Hz, which demonstrates that the human PIR signal is more orderly.
(3) Because the real wavelet decomposition is shift sensitve, that is, small fluctuation will lead to unpredictable results. Double density dual tree complexity wavelet transform (DD-DT CWT) wavelet entropy is proposed for extracting features of PIR signals. DD-DT CWT has good properties of shift-invariant, anti aliasing and high calculation efficiency. Therefore, DD-DT CWT wavelet entropy preserves the properties of approximately periodic of PIR signal, and extracts features which are can be used as discrimination information for different infrared soruces. Experimental results show that the recognition rate is 87.3% when the decomposition level is 4.
(4) In order to improve the recognition ability, canonical correlation analysis (CCA) for PIR signal feature fusion is proposed. The proposed method uses correlation features of two groups of feature vectors as effective discriminant information, so it is not only suitable for information fusion, but also eliminates the redundant information among the features. This is a new way to classifiacation and recongiton for PIR signals. Better feature description for classification can be obtained by fusing the local and global features of PIR signals. Experimental results show the recognition rate of DD-DT CWT wavelet entropy fusing with its own sub-pattern based on CCA method can reach to 94.3%, which is 7.0% higher than that of using single feature.
The three feature extraction and recognition methods proposed in this dissertation are effective for improving the performance of the existing PIR detector. In addition, by the comparison of the three methods, the CCA feature fusion method has the best human recognition ability and the lowest false alarm rate.
引文
[1] A. L.Cour-Harbo , Geometric modeling of a two-dimensional sensor array for determining spatial position of a passive object [J]. IEEE Sensors J., 2004, 4 (5):627-642.
[2] N. Kakuta,S.Yokoyama, M.Nakamura. Estimation of radiative heat transfer using a geometric human model [J].IEEE. Trans. Biomed.Eng.2001.48:324-331.
[3] S. T. Liu, Donald Long. Pyroelectric detector and materials [C].Proceedings of the IEEE, 1978,66(1):14-26.
[4] Akram Hossain, Muhanmmad H. Rashid. Pyroelectric detectors and their applications [J].IEEE Transaction on Industry Applications, 1991, 27(5):824-829.
[5] C. C. Chang, C. S. Tang. An integrated pyroelectric infrared sensor with a PZT thin film [J]. Sensors and Actuators A, 1998,65:171-174.
[6] Vladimir B. Samoilov, Yung Sup Yoon. Frequency response of multilayer pyroelectric sensors [J]. IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency control, 1998,45(5):1246-1254.
[7] Seong Jun Kang, Vladimir B. Samoilov, Yung Sup Yoon. Low-Frequency response of pyroelectric sensors [J]. IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency control, 1998,45(5):1255-1260.
[8] Weiguo Liu, Lingling Sun, Weiguang Zhu,et al. Noise and specific detectivity of pyroelectric detectors using lead titanate zironate (PZT) thin films [J]. Microelectronic Engineering, 2003,66:785-791.
[9] Ernesto Suaste-Gomez, Ruben Gonzalez-ballesteros, Victor Castillo-Rivas. Pyroelectric properities of Pb0.88Ln0.08Ti0.98Mn0.02O3(Ln=La,Sm,Eu) ferroelectric ceramic system [J]. Materials Characterization, 2003,50:349-352.
[10]徐克宝,曹建等.一种高灵敏度自主采样式热释电传感器的研究[J].仪器仪表学报,2006,27(12):1742-1745.
[11]熊涛,丁辛芳等.一种新颖的照明控制电路[J].传感器技术,1999,18,(5):50-53.
[12] D.Cima. Using Lithium Tantalate Pyroelectric Detectors in Robotics Applications [J]. Eletec Instruments. Inc. Daytona Beach,FL (Oct.1994).
[13] M.Betke, L.Gurvits. Mobile robot localization using landmarks [J]. IEEE Trans. On Robotics and Automation.1997.13(2):251-263.
[14] S.I.Roumeliotis, G.A.Bekey. Distributed multirobot localization [J]. IEEE Trans. On Robotics and Automation. 2002.18(5):781-795.
[15] R.Morlok, M.Gini. Dispersing robots in an unknown environment [C].In Proc.of the 7th Int’l Symposium on Distributed Autonomous Robotic System, June 2004.
[16] Monica Anderson Lapoint,Ian Burt,Kelly Cannon,et al.,Relative Collaborative Localization Using Pyroelectric Sensors [C]. Intelligent Robots and Systems,2005.
[17]吴英才,林华清.PIR传感器在防盗系统中的应用[J].传感器技术,2002,21(7):47-48.
[18]王松德,梁会琴,张须欣,等.热释电红外传感器在无线遥控报警系统中的应用[J].光谱学与光谱分析,2007,27(6):1124-1126.
[19]张兴周,苏运东.PIR探测警戒系统[J].传感器技术.1997,16(5):38-40.
[20]王松德,张栓记,朱小龙等.PIR探测器在安全防护系统中的应用研究[J].光谱学与光谱分析, 2006,26(11):2027-2029.
[21]于耀东.基于多传感器信息融合技术的汽车防盗系统研究[D].大连理工大学硕士学位论文,2005.
[22]吴顺伟,朱丽娜等.基于热释电传感器的位置相关算法研究[J].山东农业大学学报(自然科学版)2006,37,(3):449-452.
[23]吴董炯.一种热释电红外线传感器的应用[J].上海电机学院学报, 2005,8(6):18-21.
[24]王均福,袁红霞,王景聚.热释电红外传感器在防入侵方面的研究[J].哈尔滨师范大学自然科学学报,2008,24(1):31-34.
[25]张敏.基于红外传感器的人体信号检测保护系统设计[J].装备制造技术,2008,1:50-52.
[26]黄翔东.热释电成像及其图像后处理的设计和研究[D].天津大学硕士学位论文,2004.
[27]郭建斌.红外热释电效应在火焰探测中的应用[J].中国科技信息,2008,1:270-271.
[28] R.W.Asheimer, F.Schwarz. Thermal imaging using pyroelectric detectors [J]. Appl. Opt. 1968,7(9):1687-1696.
[29] Nobuyuki Yoshiike, Koji Arita, Katsuya Morinaka,et al., Human information sensor [J]. Sensors and Actuators A .1995, 48:73-78.
[30] Kauhiko Hashimoto,Makoto Yoshinomoto,Satoshi Matsueda,el al., Development of people-counting system with human-information sensor using multi-element pyroelectric infrared array detector [J]. Sensors and Actuators A.1997,58:165-171.
[31] Katsuya Morinaka, Kazuhiko Hashimoto,Shinji Tanaka, et al. Human information sensor [J]. Sensors and Actuators A.1998,66:1-8.
[32] M.R.Cabrer, R.P.D.Redondo,A.F.Vilas,et al.Controlling the Smart Home from TV [J].IEEE Transactions on Consumer Electronics.2006,52(2):421-429.
[33] K.C. Lee and H.H Lee. Network-based fire-detection system via controller area network for smart home automation [J]. IEEE Transactions on Consumer Electronics. 2004,50(4):1093-1100.
[34] Kyoung Nam Ha, Kyung Chang Lee, Suk Lee. Development of PIR sensor based indoor location detection system for smart home [C].SICE-ICASE international Joint Conference 2006,Oct.18-21,2006 in Bexo,Busan,Korea.2162-2167.
[35] Sunita Ram,Jennie Sharf. The People Sensor A Mobility aid for the Visually Impaired [J].IEEE. 1998:166-167.
[36] Alka R Kaushik,Branko G celler. Characterization of passive infrared sensors for monitoring occupancy pattern and functional health status of elderly people living alone at home [C]. Proceeding for the 28th IEEE EMBS Annual International Conference, New York City, USA. Aug.2006:5257-5260.
[37] Katek M., Sosnowski Tadeusz Piatkowski. Passive infrared detector used for detection of very slowly moving or crawling people [J]. Optoelectronics Review, 2008,16 (3):77-84.
[38] Tomasz Sosnowski, HenrykMadura, Mariusz Kastek, et al.. Method of objects detection employing passive IR detectors for security systems [C]. Electro-Optical and Infrared Systems: Technology and Applications V. Proc. Of SPIE 7113:71131G.-1-10.
[39] J.S.Fang,Q. Hao, David J. Brady,.et al.. Path-dependent human identification using a Pyroelectric infrared sensor and Fresnel lens arrays [J]. Optics Express,2006.14(2):609-624.
[40] J.S.Fang,Q. Hao, David J. Brady,.et al.. Pyroelectric infrared biometric system for real-time walker recognition by use of maximum likelihood principal components estimation (MLPCE) method [J]. Optics Express.2007,15(6):3271-3284.
[41] J.S.Fang,Q. Hao, David J. Brady,.et al.. Real-time human identification using a pyroelectric infrared detector array and hidden Markov models [J].Optics Express.2006,14:6643-6658.
[42]吕小平.热释电探测器综合理论模型分析[D].长春理工大学硕士学位论文,2006.
[43]高爱华,刘卫国,张伟等.热释电探测器特征参数动态响应测量[J].西安工业大学学报,2007,27(3):205-208.
[44]付文羽.热释电红外传感器噪声特性分析[J].传感器技术,2001,20(8):25-27.
[45]徐浦.入侵探测器技术讲座之一被动红外入侵探测器的光学系统(上)[J]. A&S安防工程商,2005,2:112-115.
[46]陈永甫.红外探测与控制电路[M].人民邮电出版社,2004.
[47]陈永甫.红外辐射红外器件与典型应用[M].人民邮电出版社,2004.
[48]徐浦.入侵探测器技术讲座之五-热释电传感器的频率响应[J]. A&S安防工程商,2005,9:114-117.
[49]苏桂平,刘争春,姚旭初等.一种信息安全系统中序列随机性检验方法[J].计算机工程,2006,32(8):153-154.
[50]范丽敏,冯登国,陈华.随机性检测参数选择研究[J].通信学报,2009,30(1):1-6.
[51]吴怀宇.时间序列分析与综合[M].武昌:武汉大学出版社,2004.
[52] Donoho D L, Johnstone I M. Ideal Spatial Adaptation via Wavelet Shrinkage [J]. Biometrika, 1994, 81: 425–455.
[53] Donoho D L, Johnstone I M. Adaptation to Unkown Smoothness via Wavelet Shrinkage [J]. Journal of American statistical Accoc, 1995, 90(432): 1200-1224.
[54] Chang S Grace, Yu Bin, Vetterli Martin. Adaptive Wavelet Thresholding for Image Denoising and Compression [J]. IEEE Trans. Image Processing, 2000, 9(9): 1532-1546.
[55] Sendur Levent, Selesnick I W. Bivariate Shrinkage Functions for Wavelet-based Denoising Exploiting Interscale Dependency [J]. IEEE Transactions on Signal Processing, 2002, 50(11): 2744-2756.
[56] Xu Y.,Weaver J.B.,Healy D.M.,et al.. Wavelet transform filters: a spatially selective noise filtration technique. IEEE IEEETRANSFORMS ON IMAGE PROCESSING, 1994,3(6):747-758
[57]陈强,黄声亭,王韦.小波去噪效果评价的另一指标[J].测绘信息与工程,2008,33(5):13-14.
[58] Clercq W D, Vergult A, Vanrumste B,et al.. Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram[J]. IEEE Transactions on Biomedical engineering, 2006, 53(12):2583-2587.
[59] Wang Pengtao, Chen Jianguo, Ma Biaojiang. The study of feature extraction using local energy of frequency bands based on wavelet packet decomposition [C]. The 3nd International Conference on Natural Comutation, IEEE Computer Society. HaiKou, China. August 25-27,2007:749-752.
[60] R. R. Coifman, M. V. Wickerhauser. Entropy-based algorithms for best basis selection[J]. IEEE Trans. On Information Theory,1992,38(2):713-718.
[61] Samjin Choi. Detection of valvular heart disorders using wavelet packet decomposition and support vector machine[J]. Expert systems with applications, 2008,35:1679-1687.
[62] Shensa M.J.. Discrete wavelet transforms: wedding theàtrous and Mallat algorithms [J]. IEEE Transactions on Signal Processing.1992,40(10):2462-2482.
[63] Alex Aussem, Fionn Murtagh..Combing neural network forecasts on wavelet-transformed time series [J]. Connection Science,1997,10(1):113-121.
[64] Peter R., Kingsbury N. G.. Complex Wavelets Features for Fast Texture Image Retrieval [C]. IEEE International Conference on Image Processing, 1999,1:109-113.
[65] Adeel Mumtaz, S A M Gilani, Tahir Jameel. A Novel Color Image Retrieval System based on Dual Tree Complex Wavelet Transform and Support Vector Machines [C]. 10th IEEEInternational Multitopic Conference, 2006: 163-168.
[66] Adeel Mumtaz, S A M Gilani, Tahir Jameel. A Novel Texture Image Retrieval System based on Dual Tree Complex Wavelet Transform and Support Vector Machines [C]. 2nd International Conference on Emerging Technologies, 2006: 108-114.
[67]李海燕,刘国栋,刘炳国,等.双密度小波在表面形貌信号分离中的应用[J].光学精密工程, 2008, 16(6): 1093-1097.
[68] Sveinsson Johannes R, Benediktsson Jon Atli. Double Density Wavelet Transformation for Speckle Reduction of SAR Images [C]. International Geoscience and Remote Sensing Symposium (IGARSS), 2002, 1: 113-115.
[69] Qiao Yu-Long, Song Chun-Yan, Zhao Chun-Hui. Double-Density Discrete Wavelet Transform based Texture Classification [C]. Proc. - Int. Conf. Int. Inf. Hiding Multimedia Signal Process., IIHMSP, 2007, 1: 91-94.
[70]李鹏,喻罡,冀晓燕,等.基于双密度双树复小波变换的超声图像降噪[J].系统仿真学报, 2007, 19(24): 5797-5801.
[71] Hewer Gary A, Kuo Wei, Hanson Grant, et al.. Double density Complex Wavelet based Image Cartoon-texture Decomposition [C]. Conference Record - Asilomar Conference on Signals, Systems and Computers, 2006: 861-868.
[72] Selesnick I W. The Double-Density Dual-Tree DWT [J]. IEEE Transactions on Signal Processing, 2004, 52(5): 1304-1314.
[73] C.E. Shannon.A mathematical theory of communication [J].Reprinted with correction form The Bell System Technical Journal.1948,27:379-423,623-656.
[74] Robert M. Gray. Entropy and information theory [M].New York: Springer-Verlag, 2009.
[75] Natwong B, Sooraksa P, Pintavirooj C, et al.. Wavelet Entropy Analysis of the High Resolution ECG [C]. 2006 1st IEEE Conference on Industrial Electronics and Applications, 2006.
[76] Hui Liu, GuoHai Liu, Yue Shen. Lifting Wavelet Scheme and Wavelet Energy Entropy Theory for Transient Power Quality Detection [C]. Proceedings of the 7th World Congress on Intelligent Control and Automation, 2008: 6011-6016.
[77] Weixin Ren, Zengshou Sun. Structural damage identification by using wavelet entropy [J]. Engineering Structures, 2008, 30(10):2840-2849.
[78]陈伟婷.基于熵的表面肌电信号特征提取研究[D].上海交通大学博士论文,2008.
[79] Vapnik V.N.. Statistical Learning Theory [M].New York:John Wiley and Sons Inc., 1998.
[80] Vapnik V.N.. An overview of statistical learning theory [J].IEEE Transaction on Neural Networks, 1999,10(5):988-999.
[81] Jain A.K., Duin R.P.W., Mao J.. Statistical pattern recognition: a review [J]. IEEE Transactions on Pattern Analysis Machine Intelligence,2000,22:34-37.
[82] Suykens J., Vandewalle J.. Least squares support vector machine classifiers [J].Neural Processing Letters,1999, 9(3):293-300.
[83] Van G. T., Suykens J., Lanckriet G. et al.. Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel fisher discriminant analysis [J]. Neural computation.2002,15(5):1115-1148.
[84] Van G. T., Suykens J., Baesens B., et al.. Beanchmarking least squrares support vector machine classifiers [J].Machine Learning,2004,54(1):5-32.
[85] Jiao L. C., Bo L. F., Wang L.. Fast sparse approximation for least squares support vector machine [J]. IEEE Transactions on Neural Networks,2007,18(2):685-679.
[86] Vapnik V. N. The Nature of Statistical Learning Theory [M]. Berlin: Springer-Verlag, 1995.
[87]程泽凯,林士敏.文本分类器稳定性评估研究[J].情报学报, 2005,24(1):64-68.
[88]韩崇昭,朱洪艳,段战胜.多源信息融合[M].北京:清华大学出版社, 2006.
[89] David L. Hall, James Llinas. Handbook of multisensor data fusion [M]. CRC Press,Boca Raton London New York Washington, D.C., 2001.
[90] Dietterich T G, Bakiri G. Solving multiclass learning problems via error-correcting output codes [J].Journal of Artificial Intelligence Research,1995,2:263-286.
[91] Dietterich T G. Ensemble methods in machine learning [J].Lecture Notes in Computer Science,2000,1857:1-15.
[92] Dietterich T G. An experimental comparision of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization [J]. Machine Learning,2000,40(2):139-157
[93] Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: bagging,boosting,and variants [J].Machine Learning,1999,36(1-2):105-139.
[94] J.Yang, J.Y.Yang, D.Zhang, et al.. Feature fusion: parallel strategy vs. serial strategy [J].Pattern Recognition,2003,36:1369-1381.
[95] C.J.Liu, H.Wechsler. A shape-and texture-based enhanced Fisher classifier for face recognition [J].IEEE Trans. Image Process,2001,10(4):598-608.
[96] J.Yang, J.Y.Yang. Generalized K–L transform based combined feature extraction [J].Pattern Recognition,2002, 35(1):295-297.
[97] Sun Q S, Zeng S G., Liu Y, et al.. A new method of feature fusion and it’s application in image recognition [J]. Pattern Recognition,2005, 38(12):2437-2448.
[98]孙权森,曾生根,王平安等.典型相关分析的理论及其在特征融合中的应用[J].计算机学报, 2005, 28(9):1524-1533.
[99] Sun Q S, Zeng S G., Liu Y, et al.. Feature fusion method based on canonical correlation analysis and handwritten character recognition [C]. In proceedings of the 8th International Conference on Control, Automation, Robotics and Vision, Kunming, China IEEEE, 2004.1547-1552.
[100] M. Borga. Learning Multidimensional Signal Processing [D]. Link ping Studies in Science and Technolog Dissertations, Department of Electrical Engineering, Link ping University, Link ping, Sweden, 1998,531.
[101]洪泉,陈松灿,倪雪蕾.子模式典型相关分析及其在人脸识别中的应用[J].自动化学报,2008,34(1):21-30.
[102] Breiman L. Bagging predictors [J].Machine Learning,1996,24(2):123-140.
[103] Freund Y. Boosting a weak algorithm by majority [J].Information and Computation, 1995, 121(2): 256-285.
[104] Hansen,L K,Salamon P. Neural network ensembles [J].IEEE Transations on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001.
[105] Ho T H. The random subspace method for constructing decision forests [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(8):832-844.
[106] Kwok S W, Carter C. Multiple decision trees [J]. Uncertainty in Artificial Intelligence, 1990,4:327-335.
[107] Anthony M. Probabilistic analysis of learning in artificial neural networks: the PAC model and its variants [J]. Neural Computing Surveys,1997,1:1-47.
[108] Schapire R E. The strength of weak learnability [J].Machine Learning,1990,5(2):197-227.
[109] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting [J].Journal of Computer and System Sciences,1997,55(1): 119-139.
[110] Wickramaratna J,Holden S,Buxton B. Performance degradation in boosting [C].In:Kittler J,Roli F,Eds .Proceedings of the 2nd International Workshop on Multiple Classifier Systems,Lecture Notes in Computer Science.Berlin:Springer,2001, 2096:11–21.
[111] Opitz D, Maclin R. Popular Ensemble Methods: An empirical study [J]. Journal of Artificial Intelligence Research,1999,11:169-198.
[112] Opitz D. Feature selection for ensembles [C]. In Proceedings of the 16th National Conference on Artificial Intelligence,1999,379-384.
[113] Kearns M, Valiant L G. Learning Boolean formulae or factoring. Aiken Computation Laboratory,Harvard University,Cambridge,MA,Technical Report: TR21488,1988.
[114] Cunningham P, Carney J. Diversity versus quality in classification ensembles based on feature selection [C].In:López de Mántaras R,Plaza E,Eds.The 11th European Conference on Machine Learning.LNCS,Springer-Verlag Heidelberg,2000, 1810:109-116.
[115] Kuncheva L I, Whitaker C J. Measures of diversity in classifier ensembles [J]. Machine Learning, 2003,51:181-207.
[116] Shipp C A, Kuncheva L I. Relationships between combination methods and measures of diversity in combining classifiers [J]. Information fusion,2002,3(2):135-148.
[2] N. Kakuta,S.Yokoyama, M.Nakamura. Estimation of radiative heat transfer using a geometric human model [J].IEEE. Trans. Biomed.Eng.2001.48:324-331.
[3] S. T. Liu, Donald Long. Pyroelectric detector and materials [C].Proceedings of the IEEE, 1978,66(1):14-26.
[4] Akram Hossain, Muhanmmad H. Rashid. Pyroelectric detectors and their applications [J].IEEE Transaction on Industry Applications, 1991, 27(5):824-829.
[5] C. C. Chang, C. S. Tang. An integrated pyroelectric infrared sensor with a PZT thin film [J]. Sensors and Actuators A, 1998,65:171-174.
[6] Vladimir B. Samoilov, Yung Sup Yoon. Frequency response of multilayer pyroelectric sensors [J]. IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency control, 1998,45(5):1246-1254.
[7] Seong Jun Kang, Vladimir B. Samoilov, Yung Sup Yoon. Low-Frequency response of pyroelectric sensors [J]. IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency control, 1998,45(5):1255-1260.
[8] Weiguo Liu, Lingling Sun, Weiguang Zhu,et al. Noise and specific detectivity of pyroelectric detectors using lead titanate zironate (PZT) thin films [J]. Microelectronic Engineering, 2003,66:785-791.
[9] Ernesto Suaste-Gomez, Ruben Gonzalez-ballesteros, Victor Castillo-Rivas. Pyroelectric properities of Pb0.88Ln0.08Ti0.98Mn0.02O3(Ln=La,Sm,Eu) ferroelectric ceramic system [J]. Materials Characterization, 2003,50:349-352.
[10]徐克宝,曹建等.一种高灵敏度自主采样式热释电传感器的研究[J].仪器仪表学报,2006,27(12):1742-1745.
[11]熊涛,丁辛芳等.一种新颖的照明控制电路[J].传感器技术,1999,18,(5):50-53.
[12] D.Cima. Using Lithium Tantalate Pyroelectric Detectors in Robotics Applications [J]. Eletec Instruments. Inc. Daytona Beach,FL (Oct.1994).
[13] M.Betke, L.Gurvits. Mobile robot localization using landmarks [J]. IEEE Trans. On Robotics and Automation.1997.13(2):251-263.
[14] S.I.Roumeliotis, G.A.Bekey. Distributed multirobot localization [J]. IEEE Trans. On Robotics and Automation. 2002.18(5):781-795.
[15] R.Morlok, M.Gini. Dispersing robots in an unknown environment [C].In Proc.of the 7th Int’l Symposium on Distributed Autonomous Robotic System, June 2004.
[16] Monica Anderson Lapoint,Ian Burt,Kelly Cannon,et al.,Relative Collaborative Localization Using Pyroelectric Sensors [C]. Intelligent Robots and Systems,2005.
[17]吴英才,林华清.PIR传感器在防盗系统中的应用[J].传感器技术,2002,21(7):47-48.
[18]王松德,梁会琴,张须欣,等.热释电红外传感器在无线遥控报警系统中的应用[J].光谱学与光谱分析,2007,27(6):1124-1126.
[19]张兴周,苏运东.PIR探测警戒系统[J].传感器技术.1997,16(5):38-40.
[20]王松德,张栓记,朱小龙等.PIR探测器在安全防护系统中的应用研究[J].光谱学与光谱分析, 2006,26(11):2027-2029.
[21]于耀东.基于多传感器信息融合技术的汽车防盗系统研究[D].大连理工大学硕士学位论文,2005.
[22]吴顺伟,朱丽娜等.基于热释电传感器的位置相关算法研究[J].山东农业大学学报(自然科学版)2006,37,(3):449-452.
[23]吴董炯.一种热释电红外线传感器的应用[J].上海电机学院学报, 2005,8(6):18-21.
[24]王均福,袁红霞,王景聚.热释电红外传感器在防入侵方面的研究[J].哈尔滨师范大学自然科学学报,2008,24(1):31-34.
[25]张敏.基于红外传感器的人体信号检测保护系统设计[J].装备制造技术,2008,1:50-52.
[26]黄翔东.热释电成像及其图像后处理的设计和研究[D].天津大学硕士学位论文,2004.
[27]郭建斌.红外热释电效应在火焰探测中的应用[J].中国科技信息,2008,1:270-271.
[28] R.W.Asheimer, F.Schwarz. Thermal imaging using pyroelectric detectors [J]. Appl. Opt. 1968,7(9):1687-1696.
[29] Nobuyuki Yoshiike, Koji Arita, Katsuya Morinaka,et al., Human information sensor [J]. Sensors and Actuators A .1995, 48:73-78.
[30] Kauhiko Hashimoto,Makoto Yoshinomoto,Satoshi Matsueda,el al., Development of people-counting system with human-information sensor using multi-element pyroelectric infrared array detector [J]. Sensors and Actuators A.1997,58:165-171.
[31] Katsuya Morinaka, Kazuhiko Hashimoto,Shinji Tanaka, et al. Human information sensor [J]. Sensors and Actuators A.1998,66:1-8.
[32] M.R.Cabrer, R.P.D.Redondo,A.F.Vilas,et al.Controlling the Smart Home from TV [J].IEEE Transactions on Consumer Electronics.2006,52(2):421-429.
[33] K.C. Lee and H.H Lee. Network-based fire-detection system via controller area network for smart home automation [J]. IEEE Transactions on Consumer Electronics. 2004,50(4):1093-1100.
[34] Kyoung Nam Ha, Kyung Chang Lee, Suk Lee. Development of PIR sensor based indoor location detection system for smart home [C].SICE-ICASE international Joint Conference 2006,Oct.18-21,2006 in Bexo,Busan,Korea.2162-2167.
[35] Sunita Ram,Jennie Sharf. The People Sensor A Mobility aid for the Visually Impaired [J].IEEE. 1998:166-167.
[36] Alka R Kaushik,Branko G celler. Characterization of passive infrared sensors for monitoring occupancy pattern and functional health status of elderly people living alone at home [C]. Proceeding for the 28th IEEE EMBS Annual International Conference, New York City, USA. Aug.2006:5257-5260.
[37] Katek M., Sosnowski Tadeusz Piatkowski. Passive infrared detector used for detection of very slowly moving or crawling people [J]. Optoelectronics Review, 2008,16 (3):77-84.
[38] Tomasz Sosnowski, HenrykMadura, Mariusz Kastek, et al.. Method of objects detection employing passive IR detectors for security systems [C]. Electro-Optical and Infrared Systems: Technology and Applications V. Proc. Of SPIE 7113:71131G.-1-10.
[39] J.S.Fang,Q. Hao, David J. Brady,.et al.. Path-dependent human identification using a Pyroelectric infrared sensor and Fresnel lens arrays [J]. Optics Express,2006.14(2):609-624.
[40] J.S.Fang,Q. Hao, David J. Brady,.et al.. Pyroelectric infrared biometric system for real-time walker recognition by use of maximum likelihood principal components estimation (MLPCE) method [J]. Optics Express.2007,15(6):3271-3284.
[41] J.S.Fang,Q. Hao, David J. Brady,.et al.. Real-time human identification using a pyroelectric infrared detector array and hidden Markov models [J].Optics Express.2006,14:6643-6658.
[42]吕小平.热释电探测器综合理论模型分析[D].长春理工大学硕士学位论文,2006.
[43]高爱华,刘卫国,张伟等.热释电探测器特征参数动态响应测量[J].西安工业大学学报,2007,27(3):205-208.
[44]付文羽.热释电红外传感器噪声特性分析[J].传感器技术,2001,20(8):25-27.
[45]徐浦.入侵探测器技术讲座之一被动红外入侵探测器的光学系统(上)[J]. A&S安防工程商,2005,2:112-115.
[46]陈永甫.红外探测与控制电路[M].人民邮电出版社,2004.
[47]陈永甫.红外辐射红外器件与典型应用[M].人民邮电出版社,2004.
[48]徐浦.入侵探测器技术讲座之五-热释电传感器的频率响应[J]. A&S安防工程商,2005,9:114-117.
[49]苏桂平,刘争春,姚旭初等.一种信息安全系统中序列随机性检验方法[J].计算机工程,2006,32(8):153-154.
[50]范丽敏,冯登国,陈华.随机性检测参数选择研究[J].通信学报,2009,30(1):1-6.
[51]吴怀宇.时间序列分析与综合[M].武昌:武汉大学出版社,2004.
[52] Donoho D L, Johnstone I M. Ideal Spatial Adaptation via Wavelet Shrinkage [J]. Biometrika, 1994, 81: 425–455.
[53] Donoho D L, Johnstone I M. Adaptation to Unkown Smoothness via Wavelet Shrinkage [J]. Journal of American statistical Accoc, 1995, 90(432): 1200-1224.
[54] Chang S Grace, Yu Bin, Vetterli Martin. Adaptive Wavelet Thresholding for Image Denoising and Compression [J]. IEEE Trans. Image Processing, 2000, 9(9): 1532-1546.
[55] Sendur Levent, Selesnick I W. Bivariate Shrinkage Functions for Wavelet-based Denoising Exploiting Interscale Dependency [J]. IEEE Transactions on Signal Processing, 2002, 50(11): 2744-2756.
[56] Xu Y.,Weaver J.B.,Healy D.M.,et al.. Wavelet transform filters: a spatially selective noise filtration technique. IEEE IEEETRANSFORMS ON IMAGE PROCESSING, 1994,3(6):747-758
[57]陈强,黄声亭,王韦.小波去噪效果评价的另一指标[J].测绘信息与工程,2008,33(5):13-14.
[58] Clercq W D, Vergult A, Vanrumste B,et al.. Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram[J]. IEEE Transactions on Biomedical engineering, 2006, 53(12):2583-2587.
[59] Wang Pengtao, Chen Jianguo, Ma Biaojiang. The study of feature extraction using local energy of frequency bands based on wavelet packet decomposition [C]. The 3nd International Conference on Natural Comutation, IEEE Computer Society. HaiKou, China. August 25-27,2007:749-752.
[60] R. R. Coifman, M. V. Wickerhauser. Entropy-based algorithms for best basis selection[J]. IEEE Trans. On Information Theory,1992,38(2):713-718.
[61] Samjin Choi. Detection of valvular heart disorders using wavelet packet decomposition and support vector machine[J]. Expert systems with applications, 2008,35:1679-1687.
[62] Shensa M.J.. Discrete wavelet transforms: wedding theàtrous and Mallat algorithms [J]. IEEE Transactions on Signal Processing.1992,40(10):2462-2482.
[63] Alex Aussem, Fionn Murtagh..Combing neural network forecasts on wavelet-transformed time series [J]. Connection Science,1997,10(1):113-121.
[64] Peter R., Kingsbury N. G.. Complex Wavelets Features for Fast Texture Image Retrieval [C]. IEEE International Conference on Image Processing, 1999,1:109-113.
[65] Adeel Mumtaz, S A M Gilani, Tahir Jameel. A Novel Color Image Retrieval System based on Dual Tree Complex Wavelet Transform and Support Vector Machines [C]. 10th IEEEInternational Multitopic Conference, 2006: 163-168.
[66] Adeel Mumtaz, S A M Gilani, Tahir Jameel. A Novel Texture Image Retrieval System based on Dual Tree Complex Wavelet Transform and Support Vector Machines [C]. 2nd International Conference on Emerging Technologies, 2006: 108-114.
[67]李海燕,刘国栋,刘炳国,等.双密度小波在表面形貌信号分离中的应用[J].光学精密工程, 2008, 16(6): 1093-1097.
[68] Sveinsson Johannes R, Benediktsson Jon Atli. Double Density Wavelet Transformation for Speckle Reduction of SAR Images [C]. International Geoscience and Remote Sensing Symposium (IGARSS), 2002, 1: 113-115.
[69] Qiao Yu-Long, Song Chun-Yan, Zhao Chun-Hui. Double-Density Discrete Wavelet Transform based Texture Classification [C]. Proc. - Int. Conf. Int. Inf. Hiding Multimedia Signal Process., IIHMSP, 2007, 1: 91-94.
[70]李鹏,喻罡,冀晓燕,等.基于双密度双树复小波变换的超声图像降噪[J].系统仿真学报, 2007, 19(24): 5797-5801.
[71] Hewer Gary A, Kuo Wei, Hanson Grant, et al.. Double density Complex Wavelet based Image Cartoon-texture Decomposition [C]. Conference Record - Asilomar Conference on Signals, Systems and Computers, 2006: 861-868.
[72] Selesnick I W. The Double-Density Dual-Tree DWT [J]. IEEE Transactions on Signal Processing, 2004, 52(5): 1304-1314.
[73] C.E. Shannon.A mathematical theory of communication [J].Reprinted with correction form The Bell System Technical Journal.1948,27:379-423,623-656.
[74] Robert M. Gray. Entropy and information theory [M].New York: Springer-Verlag, 2009.
[75] Natwong B, Sooraksa P, Pintavirooj C, et al.. Wavelet Entropy Analysis of the High Resolution ECG [C]. 2006 1st IEEE Conference on Industrial Electronics and Applications, 2006.
[76] Hui Liu, GuoHai Liu, Yue Shen. Lifting Wavelet Scheme and Wavelet Energy Entropy Theory for Transient Power Quality Detection [C]. Proceedings of the 7th World Congress on Intelligent Control and Automation, 2008: 6011-6016.
[77] Weixin Ren, Zengshou Sun. Structural damage identification by using wavelet entropy [J]. Engineering Structures, 2008, 30(10):2840-2849.
[78]陈伟婷.基于熵的表面肌电信号特征提取研究[D].上海交通大学博士论文,2008.
[79] Vapnik V.N.. Statistical Learning Theory [M].New York:John Wiley and Sons Inc., 1998.
[80] Vapnik V.N.. An overview of statistical learning theory [J].IEEE Transaction on Neural Networks, 1999,10(5):988-999.
[81] Jain A.K., Duin R.P.W., Mao J.. Statistical pattern recognition: a review [J]. IEEE Transactions on Pattern Analysis Machine Intelligence,2000,22:34-37.
[82] Suykens J., Vandewalle J.. Least squares support vector machine classifiers [J].Neural Processing Letters,1999, 9(3):293-300.
[83] Van G. T., Suykens J., Lanckriet G. et al.. Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel fisher discriminant analysis [J]. Neural computation.2002,15(5):1115-1148.
[84] Van G. T., Suykens J., Baesens B., et al.. Beanchmarking least squrares support vector machine classifiers [J].Machine Learning,2004,54(1):5-32.
[85] Jiao L. C., Bo L. F., Wang L.. Fast sparse approximation for least squares support vector machine [J]. IEEE Transactions on Neural Networks,2007,18(2):685-679.
[86] Vapnik V. N. The Nature of Statistical Learning Theory [M]. Berlin: Springer-Verlag, 1995.
[87]程泽凯,林士敏.文本分类器稳定性评估研究[J].情报学报, 2005,24(1):64-68.
[88]韩崇昭,朱洪艳,段战胜.多源信息融合[M].北京:清华大学出版社, 2006.
[89] David L. Hall, James Llinas. Handbook of multisensor data fusion [M]. CRC Press,Boca Raton London New York Washington, D.C., 2001.
[90] Dietterich T G, Bakiri G. Solving multiclass learning problems via error-correcting output codes [J].Journal of Artificial Intelligence Research,1995,2:263-286.
[91] Dietterich T G. Ensemble methods in machine learning [J].Lecture Notes in Computer Science,2000,1857:1-15.
[92] Dietterich T G. An experimental comparision of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization [J]. Machine Learning,2000,40(2):139-157
[93] Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: bagging,boosting,and variants [J].Machine Learning,1999,36(1-2):105-139.
[94] J.Yang, J.Y.Yang, D.Zhang, et al.. Feature fusion: parallel strategy vs. serial strategy [J].Pattern Recognition,2003,36:1369-1381.
[95] C.J.Liu, H.Wechsler. A shape-and texture-based enhanced Fisher classifier for face recognition [J].IEEE Trans. Image Process,2001,10(4):598-608.
[96] J.Yang, J.Y.Yang. Generalized K–L transform based combined feature extraction [J].Pattern Recognition,2002, 35(1):295-297.
[97] Sun Q S, Zeng S G., Liu Y, et al.. A new method of feature fusion and it’s application in image recognition [J]. Pattern Recognition,2005, 38(12):2437-2448.
[98]孙权森,曾生根,王平安等.典型相关分析的理论及其在特征融合中的应用[J].计算机学报, 2005, 28(9):1524-1533.
[99] Sun Q S, Zeng S G., Liu Y, et al.. Feature fusion method based on canonical correlation analysis and handwritten character recognition [C]. In proceedings of the 8th International Conference on Control, Automation, Robotics and Vision, Kunming, China IEEEE, 2004.1547-1552.
[100] M. Borga. Learning Multidimensional Signal Processing [D]. Link ping Studies in Science and Technolog Dissertations, Department of Electrical Engineering, Link ping University, Link ping, Sweden, 1998,531.
[101]洪泉,陈松灿,倪雪蕾.子模式典型相关分析及其在人脸识别中的应用[J].自动化学报,2008,34(1):21-30.
[102] Breiman L. Bagging predictors [J].Machine Learning,1996,24(2):123-140.
[103] Freund Y. Boosting a weak algorithm by majority [J].Information and Computation, 1995, 121(2): 256-285.
[104] Hansen,L K,Salamon P. Neural network ensembles [J].IEEE Transations on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001.
[105] Ho T H. The random subspace method for constructing decision forests [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(8):832-844.
[106] Kwok S W, Carter C. Multiple decision trees [J]. Uncertainty in Artificial Intelligence, 1990,4:327-335.
[107] Anthony M. Probabilistic analysis of learning in artificial neural networks: the PAC model and its variants [J]. Neural Computing Surveys,1997,1:1-47.
[108] Schapire R E. The strength of weak learnability [J].Machine Learning,1990,5(2):197-227.
[109] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting [J].Journal of Computer and System Sciences,1997,55(1): 119-139.
[110] Wickramaratna J,Holden S,Buxton B. Performance degradation in boosting [C].In:Kittler J,Roli F,Eds .Proceedings of the 2nd International Workshop on Multiple Classifier Systems,Lecture Notes in Computer Science.Berlin:Springer,2001, 2096:11–21.
[111] Opitz D, Maclin R. Popular Ensemble Methods: An empirical study [J]. Journal of Artificial Intelligence Research,1999,11:169-198.
[112] Opitz D. Feature selection for ensembles [C]. In Proceedings of the 16th National Conference on Artificial Intelligence,1999,379-384.
[113] Kearns M, Valiant L G. Learning Boolean formulae or factoring. Aiken Computation Laboratory,Harvard University,Cambridge,MA,Technical Report: TR21488,1988.
[114] Cunningham P, Carney J. Diversity versus quality in classification ensembles based on feature selection [C].In:López de Mántaras R,Plaza E,Eds.The 11th European Conference on Machine Learning.LNCS,Springer-Verlag Heidelberg,2000, 1810:109-116.
[115] Kuncheva L I, Whitaker C J. Measures of diversity in classifier ensembles [J]. Machine Learning, 2003,51:181-207.
[116] Shipp C A, Kuncheva L I. Relationships between combination methods and measures of diversity in combining classifiers [J]. Information fusion,2002,3(2):135-148.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |